System and method for an automated sense and avoid system for an electric aircraft

ABSTRACT

A system and method for an automated sense and avoid system for an electric aircraft is illustrated. The system comprises a flight controller communicatively connected to an electric aircraft, wherein the flight controller is configured to receive a plurality of flight inputs from a sensor, determine an impact element as a function of the plurality of flight inputs, produce a flight modification as a function of the impact element, and initiate the flight modification as a function of an automated process.

FIELD OF THE INVENTION

The present invention generally relates to the field of electricaircrafts. In particular, the present invention is directed to a systemand method for an automated sense and avoid system for an electricaircraft.

BACKGROUND

In order to avoid catastrophic damages to the electric aircraft and itsworkers, a sense and avoid system automatically avoids anything thatmight collide with the aircraft and cause such harm.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for an automated sense and avoid system for anelectric aircraft is illustrated. The system comprises a flightcontroller communicatively connected to an electric aircraft, whereinthe flight controller is configured to receive a plurality of flightinputs from a sensor, identify an impact element as a function of theplurality of flight inputs, generate a navigation status as a functionof the impact element, produce a flight modification as a function ofthe navigation status, and initiate the flight modification as afunction of an automated process.

In another aspect, a method for an automated sense and avoid system foran electric aircraft is presented. The method comprises communicativelyconnecting a flight controller to an electric aircraft, receiving, atthe flight controller, a plurality of flight inputs from a sensor,identifying, at the flight controller, an impact element as a functionof the plurality of flight inputs, generating, at the flight controller,a navigation status as a function of the impact element, producing, atthe flight controller, a flight modification as a function of thenavigation status, and initiating, at the flight controller, the flightmodification as a function of an automated process.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a diagrammatic representation of an exemplary embodiment of anelectric aircraft;

FIG. 2 is a block diagram of an exemplary embodiment of a system for anautomated sense and avoid system for an electric aircraft;

FIG. 3 is a block diagram of an exemplary embodiment of a flightcontroller;

FIG. 4 is a block diagram of an exemplary embodiment of amachine-learning module;

FIG. 5 is a flow diagram of an exemplary embodiment of a method for anautomated sense and avoid system for an electric aircraft;

FIG. 6 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof; and

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. As used herein, the word “exemplary” or “illustrative” means“serving as an example, instance, or illustration.” Any implementationdescribed herein as “exemplary” or “illustrative” is not necessarily tobe construed as preferred or advantageous over other implementations.All of the implementations described below are exemplary implementationsprovided to enable persons skilled in the art to make or use theembodiments of the disclosure and are not intended to limit the scope ofthe disclosure, which is defined by the claims. For purposes ofdescription herein, the terms “upper”, “lower”, “left”, “rear”, “right”,“front”, “vertical”, “horizontal”, and derivatives thereof shall relateto the invention as oriented in FIG. 1 . Furthermore, there is nointention to be bound by any expressed or implied theory presented inthe preceding technical field, background, brief summary or thefollowing detailed description. It is also to be understood that thespecific devices and processes illustrated in the attached drawings, anddescribed in the following specification, are simply exemplaryembodiments of the inventive concepts defined in the appended claims.Hence, specific dimensions and other physical characteristics relatingto the embodiments disclosed herein are not to be considered aslimiting, unless the claims expressly state otherwise.

At a high level, aspects of the present disclosure are directed to anaircraft with an automated sense and avoid system. In an embodiment,this disclosure includes a flight controller. Aspects of the presentdisclosure include a plurality of flight inputs detected from a sensor.Aspects of the present disclosure include an impact element determinedas a function of the plurality of flight inputs. Aspects of the presentdisclosure include producing and initiating a flight modification.Exemplary embodiments illustrating aspects of the present disclosure aredescribed below in the context of several specific examples.

Referring now to FIG. 1 , an exemplary embodiment of an aircraft 100 isillustrated. In an embodiment, aircraft 100 is an electric aircraft. Asused in this disclosure an “aircraft” is any vehicle that may fly bygaining support from the air. As a non-limiting example, aircraft mayinclude airplanes, helicopters, commercial and/or recreationalaircrafts, instrument flight aircrafts, drones, electric aircrafts,airliners, rotorcrafts, vertical takeoff and landing aircrafts, jets,airships, blimps, gliders, paramotors, and the like. Aircraft 100 mayinclude an electrically powered aircraft. In embodiments, electricallypowered aircraft may be an electric vertical takeoff and landing (eVTOL)aircraft. Electric aircraft may be capable of rotor-based cruisingflight, rotor-based takeoff, rotor-based landing, fixed-wing cruisingflight, airplane-style takeoff, airplane-style landing, and/or anycombination thereof. Electric aircraft may include one or more mannedand/or unmanned aircrafts. Electric aircraft may include one or moreall-electric short takeoff and landing (eSTOL) aircrafts. For example,and without limitation, eSTOL aircrafts may accelerate plane to a flightspeed on takeoff and decelerate plane after landing. In an embodiment,and without limitation, electric aircraft may be configured with anelectric propulsion assembly. Electric propulsion assembly may includeany electric propulsion assembly as described in U.S. Nonprovisionalapplication Ser. No. 16/603,225, filed on Dec. 4, 2019, and entitled “ANINTEGRATED ELECTRIC PROPULSION ASSEMBLY,” the entirety of which isincorporated herein by reference.

Still referring to FIG. 1 , aircraft 100, may include a fuselage 104, aflight component 108 (or one or more flight components 108), computingdevice 112, and a sensor 116. Both the computing device 112 and sensor116 are described further herein with reference to FIG. 2 .

As used in this disclosure, a vertical take-off and landing (VTOL)aircraft is an aircraft that can hover, take off, and land vertically.An eVTOL, as used in this disclosure, is an electrically poweredaircraft typically using an energy source, of a plurality of energysources to power aircraft. To optimize the power and energy necessary topropel aircraft 100, eVTOL may be capable of rotor-based cruisingflight, rotor-based takeoff, rotor-based landing, fixed-wing cruisingflight, airplane-style takeoff, airplane style landing, and/or anycombination thereof. Rotor-based flight, as described herein, is wherethe aircraft generates lift and propulsion by way of one or more poweredrotors or blades coupled with an engine, such as a “quad-copter,”multi-rotor helicopter, or other vehicle that maintains its liftprimarily using downward thrusting propulsors. “Fixed-wing flight”, asdescribed herein, is where the aircraft is capable of flight using wingsand/or foils that generate lift caused by the aircraft's forwardairspeed and the shape of the wings and/or foils, such as airplane-styleflight.

Still referring to FIG. 1 , as used in this disclosure a “fuselage” is amain body of an aircraft, or in other words, the entirety of theaircraft except for a cockpit, nose, wings, empennage, nacelles, any andall control surfaces, and generally contains an aircraft's payload.Fuselage 104 may include structural elements that physically support ashape and structure of an aircraft. Structural elements may take aplurality of forms, alone or in combination with other types. Structuralelements may vary depending on a construction type of aircraft such aswithout limitation a fuselage 104. Fuselage 104 may include a trussstructure. A truss structure may be used with a lightweight aircraft andincludes welded steel tube trusses. A “truss,” as used in thisdisclosure, is an assembly of beams that create a rigid structure, oftenin combinations of triangles to create three-dimensional shapes. A trussstructure may alternatively include wood construction in place of steeltubes, or a combination thereof. In embodiments, structural elements mayinclude steel tubes and/or wood beams. In an embodiment, and withoutlimitation, structural elements may include an aircraft skin. Aircraftskin may be layered over the body shape constructed by trusses. Aircraftskin may include a plurality of materials such as plywood sheets,aluminum, fiberglass, and/or carbon fiber, the latter of which will beaddressed in greater detail later herein.

In embodiments, and with continued reference to FIG. 1 , aircraftfuselage 104 may include and/or be constructed using geodesicconstruction. Geodesic structural elements may include stringers woundabout formers (which may be alternatively called station frames) inopposing spiral directions. A “stringer,” as used in this disclosure, isa general structural element that may include a long, thin, and rigidstrip of metal or wood that is mechanically coupled to and spans adistance from, station frame to station frame to create an internalskeleton on which to mechanically couple aircraft skin. A former (orstation frame) may include a rigid structural element that is disposedalong a length of an interior of aircraft fuselage 104 orthogonal to alongitudinal (nose to tail) axis of the aircraft and may form a generalshape of fuselage 104. A former may include differing cross-sectionalshapes at differing locations along fuselage 104, as the former is thestructural element that informs the overall shape of a fuselage 104curvature. In embodiments, aircraft skin may be anchored to formers andstrings such that the outer mold line of a volume encapsulated byformers and stringers includes the same shape as aircraft 100 wheninstalled. In other words, former(s) may form a fuselage's ribs, and thestringers may form the interstitials between such ribs. The spiralorientation of stringers about formers may provide uniform robustness atany point on an aircraft fuselage such that if a portion sustainsdamage, another portion may remain largely unaffected. Aircraft skin maybe attached to underlying stringers and formers and may interact with afluid, such as air, to generate lift and perform maneuvers.

In an embodiment, and still referring to FIG. 1 , fuselage 104 mayinclude and/or be constructed using monocoque construction. Monocoqueconstruction may include a primary structure that forms a shell (or skinin an aircraft's case) and supports physical loads. Monocoque fuselagesare fuselages in which the aircraft skin or shell is also the primarystructure. In monocoque construction aircraft skin would support tensileand compressive loads within itself and true monocoque aircraft can befurther characterized by the absence of internal structural elements.Aircraft skin in this construction method is rigid and can sustain itsshape with no structural assistance form underlying skeleton-likeelements. Monocoque fuselage may include aircraft skin made from plywoodlayered in varying grain directions, epoxy-impregnated fiberglass,carbon fiber, or any combination thereof.

According to embodiments, and further referring to FIG. 1 , fuselage 104may include a semi-monocoque construction. Semi-monocoque construction,as used herein, is a partial monocoque construction, wherein a monocoqueconstruction is describe above detail. In semi-monocoque construction,aircraft fuselage 104 may derive some structural support from stressedaircraft skin and some structural support from underlying framestructure made of structural elements. Formers or station frames can beseen running transverse to the long axis of fuselage 104 with circularcutouts which are generally used in real-world manufacturing for weightsavings and for the routing of electrical harnesses and other modernon-board systems. In a semi-monocoque construction, stringers are thin,long strips of material that run parallel to fuselage's long axis.Stringers may be mechanically coupled to formers permanently, such aswith rivets. Aircraft skin may be mechanically coupled to stringers andformers permanently, such as by rivets as well. A person of ordinaryskill in the art will appreciate, upon reviewing the entirety of thisdisclosure, that there are numerous methods for mechanical fastening ofcomponents like screws, nails, dowels, pins, anchors, adhesives likeglue or epoxy, or bolts and nuts, to name a few. A subset of fuselageunder the umbrella of semi-monocoque construction includes unibodyvehicles. Unibody, which is short for “unitized body” or alternatively“unitary construction”, vehicles are characterized by a construction inwhich the body, floor plan, and chassis form a single structure. In theaircraft world, unibody may be characterized by internal structuralelements like formers and stringers being constructed in one piece,integral to the aircraft skin as well as any floor construction like adeck.

Still referring to FIG. 1 , stringers and formers, which may account forthe bulk of an aircraft structure excluding monocoque construction, maybe arranged in a plurality of orientations depending on aircraftoperation and materials. Stringers may be arranged to carry axial(tensile or compressive), shear, bending or torsion forces throughouttheir overall structure. Due to their coupling to aircraft skin,aerodynamic forces exerted on aircraft skin will be transferred tostringers. A location of said stringers greatly informs the type offorces and loads applied to each and every stringer, all of which may behandled by material selection, cross-sectional area, and mechanicalcoupling methods of each member. A similar assessment may be made forformers. In general, formers may be significantly larger incross-sectional area and thickness, depending on location, thanstringers. Both stringers and formers may include aluminum, aluminumalloys, graphite epoxy composite, steel alloys, titanium, or anundisclosed material alone or in combination.

In an embodiment, and still referring to FIG. 1 , stressed skin, whenused in semi-monocoque construction is the concept where the skin of anaircraft bears partial, yet significant, load in an overall structuralhierarchy. In other words, an internal structure, whether it be a frameof welded tubes, formers and stringers, or some combination, may not besufficiently strong enough by design to bear all loads. The concept ofstressed skin may be applied in monocoque and semi-monocoqueconstruction methods of fuselage 104. Monocoque includes only structuralskin, and in that sense, aircraft skin undergoes stress by appliedaerodynamic fluids imparted by the fluid. Stress as used in continuummechanics may be described in pound-force per square inch (lbf/in²) orPascals (Pa). In semi-monocoque construction stressed skin may bear partof aerodynamic loads and additionally may impart force on an underlyingstructure of stringers and formers.

Still referring to FIG. 1 , it should be noted that an illustrativeembodiment is presented only, and this disclosure in no way limits theform or construction method of a system and method for loading payloadinto an eVTOL aircraft. In embodiments, fuselage 104 may be configurablebased on the needs of the eVTOL per specific mission or objective. Thegeneral arrangement of components, structural elements, and hardwareassociated with storing and/or moving a payload may be added or removedfrom fuselage 104 as needed, whether it is stowed manually, automatedly,or removed by personnel altogether. Fuselage 104 may be configurable fora plurality of storage options. Bulkheads and dividers may be installedand uninstalled as needed, as well as longitudinal dividers wherenecessary. Bulkheads and dividers may be installed using integratedslots and hooks, tabs, boss and channel, or hardware like bolts, nuts,screws, nails, clips, pins, and/or dowels, to name a few. Fuselage 104may also be configurable to accept certain specific cargo containers, ora receptable that can, in turn, accept certain cargo containers.

Still referring to FIG. 1 , aircraft 100 may include a plurality oflaterally extending elements attached to fuselage 104. As used in thisdisclosure a “laterally extending element” is an element that projectsessentially horizontally from fuselage, including an outrigger, a spar,and/or a fixed wing that extends from fuselage. Wings may be structureswhich may include airfoils configured to create a pressure differentialresulting in lift. Wings may generally dispose on the left and rightsides of the aircraft symmetrically, at a point between nose andempennage. Wings may include a plurality of geometries in planform view,swept swing, tapered, variable wing, triangular, oblong, elliptical,square, among others. A wing's cross section geometry may include anairfoil. An “airfoil” as used in this disclosure is a shape specificallydesigned such that a fluid flowing above and below it exert differinglevels of pressure against the top and bottom surface. In embodiments,the bottom surface of an aircraft can be configured to generate agreater pressure than does the top, resulting in lift. Laterallyextending element may include differing and/or similar cross-sectionalgeometries over its cord length or the length from wing tip to wherewing meets aircraft's body. One or more wings may be symmetrical aboutaircraft's longitudinal plane, which includes the longitudinal or rollaxis reaching down the center of aircraft through the nose andempennage, and plane's yaw axis. Laterally extending element may includecontrols surfaces configured to be commanded by a pilot or pilots tochange a wing's geometry and therefore its interaction with a fluidmedium, like air. Control surfaces may include flaps, ailerons, tabs,spoilers, and slats, among others. The control surfaces may dispose onthe wings in a plurality of locations and arrangements and inembodiments may be disposed at the leading and trailing edges of thewings, and may be configured to deflect up, down, forward, aft, or acombination thereof. An aircraft, including a dual-mode aircraft mayinclude a combination of control surfaces to perform maneuvers whileflying or on ground.

Still referring to FIG. 1 , aircraft 100 may include a plurality offlight components 108. As used in this disclosure a “flight component”is a component that promotes flight and guidance of an aircraft. In anembodiment, flight component 108 may be mechanically coupled to anaircraft. As used herein, a person of ordinary skill in the art wouldunderstand “mechanically coupled” to mean that at least a portion of adevice, component, or circuit is connected to at least a portion of theaircraft via a mechanical coupling. Said mechanical coupling mayinclude, for example, rigid coupling, such as beam coupling, bellowscoupling, bushed pin coupling, constant velocity, split-muff coupling,diaphragm coupling, disc coupling, donut coupling, elastic coupling,flexible coupling, fluid coupling, gear coupling, grid coupling, hirthjoints, hydrodynamic coupling, jaw coupling, magnetic coupling, Oldhamcoupling, sleeve coupling, tapered shaft lock, twin spring coupling, ragjoint coupling, universal joints, or any combination thereof. In anembodiment, mechanical coupling may be used to connect the ends ofadjacent parts and/or objects of an electric aircraft. Further, in anembodiment, mechanical coupling may be used to join two pieces ofrotating electric aircraft components.

Still referring to FIG. 1 , plurality of flight components 108 mayinclude at least a lift propulsor. As used in this disclosure a“propulsor” is a component and/or device used to propel a craft upwardby exerting force on a fluid medium, which may include a gaseous mediumsuch as air or a liquid medium such as water. Propulsor may include anydevice or component that consumes electrical power on demand to propelan electric aircraft in a direction or other vehicle while on ground orin-flight. For example, and without limitation, propulsor may include arotor, propeller, paddle wheel and the like thereof. In an embodiment,propulsor may include a plurality of blades. As used in this disclosurea “blade” is a propeller that converts rotary motion from an engine orother power source into a swirling slipstream. In an embodiment, blademay convert rotary motion to push the propeller forwards or backwards.In an embodiment propulsor may include a rotating power-driven hub, towhich are attached several radial airfoil-section blades such that thewhole assembly rotates about a longitudinal axis. The lift propulsor isfurther described herein with reference to FIG. 2 .

In an embodiment, and still referring to FIG. 1 , plurality of flightcomponents 108 may include one or more power sources. As used in thisdisclosure a “power source” is a source that that drives and/or controlsany other flight component. For example, and without limitation powersource may include a motor that operates to move one or more liftpropulsor components, to drive one or more blades, or the like thereof.A motor may be driven by direct current (DC) electric power and mayinclude, without limitation, brushless DC electric motors, switchedreluctance motors, induction motors, or any combination thereof. A motormay also include electronic speed controllers or other components forregulating motor speed, rotation direction, and/or dynamic braking. Inan embodiment, power source may include an inverter. As used in thisdisclosure an “inverter” is a device that changes one or more currentsof a system. For example, and without limitation, inverter may includeone or more electronic devices that change direct current to alternatingcurrent. As a further non-limiting example, inverter may includereceiving a first input voltage and outputting a second voltage, whereinthe second voltage is different from the first voltage. In anembodiment, and without limitation, inverter may output a waveform,wherein a waveform may include a square wave, sine wave, modified sinewave, near sine wave, and the like thereof.

Still referring to FIG. 1 , power source may include an energy source.An energy source may include, for example, a generator, a photovoltaicdevice, a fuel cell such as a hydrogen fuel cell, direct methanol fuelcell, and/or solid oxide fuel cell, an electric energy storage device(e.g. a capacitor, an inductor, and/or a battery). An energy source mayalso include a battery cell, or a plurality of battery cells connectedin series into a module and each module connected in series or inparallel with other modules. Configuration of an energy sourcecontaining connected modules may be designed to meet an energy or powerrequirement and may be designed to fit within a designated footprint inan electric aircraft in which aircraft 100 may be incorporated.

In an embodiment, and still referring to FIG. 1 , an energy source maybe used to provide a steady supply of electrical power to a load overthe course of a flight by a vehicle or other electric aircraft. Forexample, the energy source may be capable of providing sufficient powerfor “cruising” and other relatively low-energy phases of flight. Anenergy source may also be capable of providing electrical power for somehigher-power phases of flight as well, particularly when the energysource is at a high SOC, as may be the case for instance during takeoff.In an embodiment, the energy source may be capable of providingsufficient electrical power for auxiliary loads including withoutlimitation, lighting, navigation, communications, de-icing, steering orother systems requiring power or energy. Further, the energy source maybe capable of providing sufficient power for controlled descent andlanding protocols, including, without limitation, hovering descent orrunway landing. As used herein the energy source may have high powerdensity where the electrical power an energy source can usefully produceper unit of volume and/or mass is relatively high. The electrical poweris defined as the rate of electrical energy per unit time. An energysource may include a device for which power that may be produced perunit of volume and/or mass has been optimized, at the expense of themaximal total specific energy density or power capacity, during design.Non-limiting examples of items that may be used as at least an energysource may include batteries used for starting applications including Liion batteries which may include NCA, NMC, Lithium iron phosphate(LiFePO4) and Lithium Manganese Oxide (LMO) batteries, which may bemixed with another cathode chemistry to provide more specific power ifthe application requires Li metal batteries, which have a lithium metalanode that provides high power on demand, Li ion batteries that have asilicon or titanite anode, energy source may be used, in an embodiment,to provide electrical power to an electric aircraft or drone, such as anelectric aircraft vehicle, during moments requiring high rates of poweroutput, including without limitation takeoff, landing, thermal de-icingand situations requiring greater power output for reasons of stability,such as high turbulence situations, as described in further detailbelow. A battery may include, without limitation a battery using nickelbased chemistries such as nickel cadmium or nickel metal hydride, abattery using lithium ion battery chemistries such as a nickel cobaltaluminum (NCA), nickel manganese cobalt (NMC), lithium iron phosphate(LiFePO4), lithium cobalt oxide (LCO), and/or lithium manganese oxide(LMO), a battery using lithium polymer technology, lead-based batteriessuch as without limitation lead acid batteries, metal-air batteries, orany other suitable battery. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various devices ofcomponents that may be used as an energy source.

Still referring to FIG. 1 , an energy source may include a plurality ofenergy sources, referred to herein as a module of energy sources. Themodule may include batteries connected in parallel or in series or aplurality of modules connected either in series or in parallel designedto deliver both the power and energy requirements of the application.Connecting batteries in series may increase the voltage of at least anenergy source which may provide more power on demand. High voltagebatteries may require cell matching when high peak load is needed. Asmore cells are connected in strings, there may exist the possibility ofone cell failing which may increase resistance in the module and reducethe overall power output as the voltage of the module may decrease as aresult of that failing cell. Connecting batteries in parallel mayincrease total current capacity by decreasing total resistance, and italso may increase overall amp-hour capacity. The overall energy andpower outputs of at least an energy source may be based on theindividual battery cell performance or an extrapolation based on themeasurement of at least an electrical parameter. In an embodiment wherethe energy source includes a plurality of battery cells, the overallpower output capacity may be dependent on the electrical parameters ofeach individual cell. If one cell experiences high self-discharge duringdemand, power drawn from at least an energy source may be decreased toavoid damage to the weakest cell. The energy source may further include,without limitation, wiring, conduit, housing, cooling system and batterymanagement system. Persons skilled in the art will be aware, afterreviewing the entirety of this disclosure, of many different componentsof an energy source.

Still referring to FIG. 1 , plurality of flight components 108 mayinclude a pusher component. As used in this disclosure a “pushercomponent” is a component that pushes and/or thrusts an aircraft througha medium. As a non-limiting example, pusher component may include apusher propeller, a paddle wheel, a pusher motor, a pusher propulsor,and the like. Additionally, or alternatively, pusher flight componentmay include a plurality of pusher flight components. Pusher componentmay be configured to produce a forward thrust. As used in thisdisclosure a “forward thrust” is a thrust that forces aircraft through amedium in a horizontal direction, wherein a horizontal direction is adirection parallel to the longitudinal axis. For example, forward thrustmay include a force of 1145 N to force aircraft to in a horizontaldirection along the longitudinal axis. As a further non-limitingexample, pusher component may twist and/or rotate to pull air behind itand, at the same time, push aircraft 100 forward with an equal amount offorce. In an embodiment, and without limitation, the more air forcedbehind aircraft, the greater the thrust force with which aircraft 100 ispushed horizontally will be. In another embodiment, and withoutlimitation, forward thrust may force aircraft 100 through the medium ofrelative air. Additionally or alternatively, plurality of flightcomponents 108 may include one or more puller components. As used inthis disclosure a “puller component” is a component that pulls and/ortows an aircraft through a medium. As a non-limiting example, pullercomponent may include a flight component such as a puller propeller, apuller motor, a tractor propeller, a puller propulsor, and the like.Additionally, or alternatively, puller component may include a pluralityof puller flight components.

Now referring to FIG. 2 , system 200 for an automated sense and avoidsystem is presented. System 200 includes a plurality of flight inputs204, flight controller 208, impact element 212, navigation status 216,and flight modification 220. In this disclosure, a “sense and avoidsystem” is a system that allows an aircraft to avoid collisions and movearound safely. It senses whether a collision is imminent and generates anew flight path, through the flight modification, in order to avoidcollision.

Referring now to FIG. 2 , system 200 includes flight controller 208communicatively connected to aircraft 100. In this disclosure, a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 208 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Flight controller 208 is furtherdescribed herein with reference to FIG. 3 .

Now referring to FIG. 2 , flight controller 208 receives a plurality offlight inputs 204 from a sensor. In this disclosure, “flight inputs” isa datum containing information about the exterior environment ofelectric aircraft. In this disclosure, “exterior environment” is thevisual surroundings and conditions of electric aircraft at any givenmoment. Plurality of flight inputs 204 may identify a specifiedgeographic location as to where an electric aircraft may be located at aparticular point in time. For example but without limitation, pluralityof flight input 204 may denote an electric aircraft is powered on,traveling at a specified altitude, powered off, grounded, stationary andthe like thereof. Plurality of flight inputs 204 may include one or moreinputs comprising a digital signal such as, but not limited to, audiosignals, video signals, radar signals, radio signals, sonar signals,lidar signals and the like thereof. For example but without limitation,plurality of flight inputs 204 may include a radar signal thatidentifies an electric aircraft's geographic location. Plurality offlight inputs 204 may include one or more topographical inputs, whereina topographical input may denote one or more structures and/or objectsthat exist in an air space that electric aircraft is traveling. A sensoror plurality of sensors may be used to detect plurality of flight inputs204.

Still referring to FIG. 2 , sensor may be used to detect plurality offlight inputs 204. As used in this disclosure a “sensor” is a device,module, and/or subsystem, utilizing any hardware, software, and/or anycombination thereof to detect events and/or changes in the instantenvironment and transmit the information; transmission may includetransmission of any wired or wireless electronic signal. Sensor may beattached, mechanically coupled, and/or communicatively coupled, asdescribed above, to aircraft. Sensor may include a torque sensor,gyroscope, accelerometer, torque sensor, magnetometer, inertialmeasurement unit (IMU), pressure sensor, force sensor, proximity sensor,displacement sensor, vibration sensor, among others. Sensor may includea sensor suite which may include a plurality of sensors that may detectsimilar or unique phenomena. For example, in a non-limiting embodiment,sensor suite may include a plurality of accelerometers, a mixture ofaccelerometers and gyroscopes, or a mixture of an accelerometer,gyroscope, and torque sensor. The herein disclosed system and method maycomprise a plurality of sensors in the form of individual sensors or asensor suite working in tandem or individually. A sensor suite mayinclude a plurality of independent sensors, as described herein, whereany number of the described sensors may be used to detect any number ofphysical or electrical quantities associated with an aircraft powersystem or an electrical energy storage system. Independent sensors mayinclude separate sensors measuring physical or electrical quantitiesthat may be powered by and/or in communication with circuitsindependently, where each may signal sensor output to a control circuitsuch as a user graphical interface. In an embodiment, use of a pluralityof independent sensors may result in redundancy configured to employmore than one sensor that measures the same phenomenon, those sensorsbeing of the same type, a combination of, or another type of sensor notdisclosed, so that in the event one sensor fails, the ability to detectphenomenon is maintained and in a non-limiting example, a user alteraircraft usage pursuant to sensor readings. Sensor may detect any dataassociated with the exterior environment of the aircraft; this includesbut is not limited to detection of another aircraft, detection of ananimal or any object in the aircraft's flight path, detection of anysort of alarming changes in weather, air pressure, etc., and the like.Sensor may be placed anywhere on the aircraft as long as view in alldirections can be detected. For example but without limitation, theremay be more than one sensor to have sensory access in all directions.

Now referring to FIG. 2 , flight controller 208 determines an impactelement 212 as a function of plurality of flight inputs 204. In thisdisclosure, an “impact element” is an obstacle, object, and/or structurethat electric aircraft may collide with and/or impact during flight.After detecting plurality of flight inputs 204, flight controller 208analyzes them and finds an impact element in the exterior environment ofaircraft 100. Determining impact element 212 may include identifying alength of time until the electric aircraft impacts and/or collides withthe object. For example, impact element 212 may denote that an electricaircraft has 2 minutes prior to colliding with an alternate electricaircraft or 4 seconds until it collides with a bird. Determining impactelement 212 may also include identifying a predictive degradation as afunction of an impact. A predictive degradation may include one or morefailure events that may be determined as a function of the collision. Inthis disclosure, a “failure event” is any event that degrades an abilityof one or more flight components, such as a broken rotor blade, adamaged lift propulsor, or even a dented fuselage. If no impact element212 is determined amongst plurality of flight inputs 204, then no flightmodification is produced nor enacted. Impact element 212 may be shown topilot on any sort of display including a primary flight display (PFD),multi-function display (MFD), heads-up display (HUD), holograph,projection, gauges, audio cues, video cues, data streams, displayed in apilot's goggles or helmet, and the like. Once impact element isdetermined, flight controller 208 generates a flight modification 220 toavoid impact element 212.

Still referring to FIG. 2 , flight controller 208 generates a navigationstatus 216 as a function of impact element 212. As used in thisdisclosure a “navigation status” is a status and/or representation of anaircraft during travel. For example, navigation status 216 may denotethat an aircraft is following a flight plan and will achieve thepredicted landing time. In an embodiment, navigations status 216 may beidentified as a function of determining a directional vector. As used inthis disclosure a “directional vector” is a data structure thatrepresents one or more a quantitative values and/or measures direction.A vector may be represented as an n-tuple of values, where n is one ormore values, as described in further detail below; a vector mayalternatively or additionally be represented as an element of a vectorspace, defined as a set of mathematical objects that can be addedtogether under an operation of addition following properties ofassociativity, commutativity, existence of an identity element, andexistence of an inverse element for each vector, and can be multipliedby scalar values under an operation of scalar multiplication compatiblewith field multiplication, and that has an identity element isdistributive with respect to vector addition, and is distributive withrespect to field addition. Each value of n-tuple of values may representa measurement or other quantitative value associated with a givencategory of data, or attribute, examples of which are provided infurther detail below; a vector may be represented, without limitation,in n-dimensional space using an axis per category of value representedin n-tuple of values, such that a vector has a geometric directioncharacterizing the relative quantities of attributes in the n-tuple ascompared to each other. Two vectors may be considered equivalent wheretheir directions, and/or the relative quantities of values within eachvector as compared to each other, are the same; thus, as a non-limitingexample, a vector represented as [5, 10, 15] may be treated asequivalent, for purposes of this disclosure, as a vector represented as[1, 2, 3]. Vectors may be more similar where their directions are moresimilar, and more different where their directions are more divergent;however, vector similarity may alternatively or additionally bedetermined using averages of similarities between like attributes, orany other measure of similarity suitable for any n-tuple of values, oraggregation of numerical similarity measures for the purposes of lossfunctions as described in further detail below. Any vectors as describedherein may be scaled, such that each vector represents each attributealong an equivalent scale of values. Each vector may be “normalized,” ordivided by a “length” attribute, such as a length attribute l as derivedusing a Pythagorean norm:

${l = \sqrt{\sum\limits_{i = 0}^{n}a_{i}^{2}}},$where a_(i) is attribute number i of the vector. Scaling and/ornormalization may function to make vector comparison independent ofabsolute quantities of attributes, while preserving any dependency onsimilarity of attributes. For example, and without limitation,directional vector may denote that aircraft is traveling with a force of300 N in a southern direction with an airspeed velocity of 400 knots.Because impact element 212 is detected to be in the way of aircraft 100,then navigation status 216 is generated in order to determine a flightmodification 220.

Referring now to FIG. 2 , flight controller 208 produces a flightmodification 220 as a function of navigation status. In this disclosure,“flight modification” is a change in the flight path or of an electricaircraft. Flight modification 220 may include a maneuver such as a turn,climb, descent, and the like thereof to be performed by the electricaircraft to maintain a safe distance from impact element 212. Flightmodification 220 may include adjusting a velocity and/or acceleration ofelectric aircraft to maintain a safe distance from impact element 212.Flight modification 220 is developed as a function of the status of theaircraft during flight, or the navigation status, and the impact element212. Flight modification 220 may include changing the altitude ofelectric aircraft to maintain a safe distance from impact element 212.Flight modification 220 may include an increased speed of other rotorsto make up for a detected damage rotor. Flight modification 220 mayinclude not moving the aircraft at all to avoid a detected impactelement 212. Producing flight modification 220 may include a machinelearning process to determine one or more flight component adjustments,wherein a machine learning process may include any machine learningprocess including but not limited to a supervised process, anunsupervised, lazy learning and the like. A “machine learning process,”as used in this disclosure, is a process that automatedly uses trainingdata to generate an algorithm that will be performed by a computingdevice/module to produce outputs. In this scenario, training data inputmay be plurality of flight inputs 204 and impact element 212 and thetraining data output may be the flight modification 220.

Referring now to FIG. 2 , flight controller 208 initiates flightmodification 216 as a function of an automated process. Flightcontroller 208 enacts flight modification 216, which means it followsthrough on the action. Flight modification 216 may be transmittedutilizing any network methodology, such as a neural network. Flightmodification 216 may be automatically initiated or may need approvalfrom a pilot before enacted. Flight modification 216 may be shown to thepilot by any of the displays mentioned above. Flight modification 216 isenacted due to an automated process; in this disclosure, an “automatedprocess” is a process that uses digital technology to perform anotherprocess automatically. In this disclosure, the process being automatedis the occurrence process of flight modification 216. Automated processmay be configured to automatedly perform flight modification without apilot intervention. Furthermore, initiating flight modification 216 mayinclude displaying flight modification 216 that is being initiated as afunction of the automated process on a display to a pilot. Pilot may beon electric aircraft or remote. Flight modification 216 is performed asa direct result of detecting impact element 212 and the process toperform flight modification 216 purposefully avoids impact element 212so no damage is done to aircraft 100 or its inhabitants.

Now referring to FIG. 3 , an exemplary embodiment 300 of a possiblecomputing device 208 is illustrated. Thus, a flight controller isillustrated. Flight controller may include and/or communicate with anycomputing device as described in this disclosure, including withoutlimitation a microcontroller, microprocessor, digital signal processor(DSP) and/or system on a chip (SoC) as described in this disclosure.Further, flight controller may include a single computing deviceoperating independently, or may include two or more computing deviceoperating in concert, in parallel, sequentially or the like; two or morecomputing devices may be included together in a single computing deviceor in two or more computing devices. In embodiments, flight controllermay be installed in an aircraft, may control the aircraft remotely,and/or may include an element installed in aircraft and a remote elementin communication therewith.

In an embodiment, and still referring to FIG. 3 , flight controller mayinclude a signal transformation component 308. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 308 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component308 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 308 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 308 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 308 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 3 , signal transformation component 308 may beconfigured to optimize an intermediate representation 312. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 308 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 308 may optimizeintermediate representation 312 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 308 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 308 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller. For example, and without limitation, native machinelanguage may include one or more binary and/or numerical languages.

In an embodiment, and without limitation, signal transformationcomponent 308 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 3 , flight controller mayinclude a reconfigurable hardware platform 316. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 316 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 3 , reconfigurable hardware platform 316 mayinclude a logic component 320. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 320 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 320 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 320 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating-point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 320 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 320 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 312. Logiccomponent 320 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller. Logiccomponent 320 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 320 may beconfigured to execute the instruction on intermediate representation 312and/or output language. For example, and without limitation, logiccomponent 320 may be configured to execute an addition operation onintermediate representation 312 and/or output language.

With continued reference to FIG. 3 , in an embodiment, and withoutlimitation, logic component 320 may be configured to calculate a flightelement 324. As used in this disclosure a “flight element” is an elementof datum denoting a relative status of aircraft. For example, andwithout limitation, flight element 324 may denote one or more torques,thrusts, airspeed velocities, forces, altitudes, groundspeed velocities,directions during flight, directions facing, forces, orientations, andthe like thereof. For example, and without limitation, flight element324 may denote that aircraft is cruising at an altitude and/or with asufficient magnitude of forward thrust. As a further non-limitingexample, flight status may denote that is building thrust and/orgroundspeed velocity in preparation for a takeoff. As a furthernon-limiting example, flight element 324 may denote that aircraft isfollowing a flight path accurately and/or sufficiently.

Still referring to FIG. 3 , flight controller may include a chipsetcomponent 328. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 328 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 320 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 328 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 320 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 328 maymanage data flow between logic component 320, memory cache, and a flightcomponent 108. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component 108 may include acomponent used to affect the aircrafts' roll and pitch which may includeone or more ailerons. As a further example, flight component 108 mayinclude a rudder to control yaw of an aircraft. In an embodiment,chipset component 328 may be configured to communicate with a pluralityof flight components as a function of flight element 324. For example,and without limitation, chipset component 328 may transmit to anaircraft rotor to reduce torque of a first lift propulsor and increasethe forward thrust produced by a pusher component to perform a flightmaneuver.

In an embodiment, and still referring to FIG. 3 , flight controller isconfigured to produce both autonomous and semi-autonomous flight. Asused in this disclosure an “autonomous function” is a mode and/orfunction of flight controller that controls aircraft automatically. Forexample, and without limitation, autonomous function may perform one ormore aircraft maneuvers, take offs, landings, altitude adjustments,flight leveling adjustments, turns, climbs, and/or descents. As afurther non-limiting example, autonomous function may adjust one or moreairspeed velocities, thrusts, torques, and/or groundspeed velocities. Asa further non-limiting example, autonomous function may perform one ormore flight path corrections and/or flight path modifications as afunction of flight element 324. In an embodiment, autonomous functionmay include one or more modes of autonomy such as, but not limited to,autonomous mode, semi-autonomous mode, and/or non-autonomous mode. Asused in this disclosure “autonomous mode” is a mode that automaticallyadjusts and/or controls aircraft and/or the maneuvers of aircraft in itsentirety. For example, autonomous mode may denote that flight controllerwill adjust the aircraft. As used in this disclosure a “semi-autonomousmode” is a mode that automatically adjusts and/or controls a portionand/or section of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller will control the ailerons and/orrudders. As used in this disclosure “non-autonomous mode” is a mode thatdenotes a pilot will control aircraft and/or maneuvers of aircraft inits entirety.

In an embodiment, and still referring to FIG. 3 , flight controller maygenerate autonomous function as a function of an autonomousmachine-learning model. Training data is used to train autonomousmachine-learning model; training data may be stored in a database orbased on expert input. Training data may include an input of the chargeof the batteries and an output of whether they need to be charged. Asused in this disclosure an “autonomous machine-learning model” is amachine-learning model to produce an autonomous function output givenflight element 324 and a pilot signal 336 as inputs; this is in contrastto a non-machine learning software program where the commands to beexecuted are determined in advance by a user and written in aprogramming language. As used in this disclosure a “pilot signal” is anelement of datum representing one or more functions a pilot iscontrolling and/or adjusting. For example, pilot signal 336 may denotethat a pilot is controlling and/or maneuvering ailerons, wherein thepilot is not in control of the rudders and/or propulsors. In anembodiment, pilot signal 336 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 336may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 336 may include an explicitsignal directing flight controller to control and/or maintain a portionof aircraft, a portion of the flight plan, the entire aircraft, and/orthe entire flight plan. As a further non-limiting example, pilot signal336 may include an implicit signal, wherein flight controller detects alack of control such as by a malfunction, torque alteration, flight pathdeviation, and the like thereof. In an embodiment, and withoutlimitation, pilot signal 336 may include one or more explicit signals toreduce torque, and/or one or more implicit signals that torque may bereduced due to reduction of airspeed velocity. In an embodiment, andwithout limitation, pilot signal 336 may include one or more localand/or global signals. For example, and without limitation, pilot signal336 may include a local signal that is transmitted by a pilot and/orcrew member. As a further non-limiting example, pilot signal 336 mayinclude a global signal that is transmitted by air traffic controland/or one or more remote users that are in communication with the pilotof aircraft. In an embodiment, pilot signal 336 may be received as afunction of a tri-state bus and/or multiplexor that denotes an explicitpilot signal should be transmitted prior to any implicit or global pilotsignal.

Still referring to FIG. 3 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller and/or a remote device may or may not use in thegeneration of autonomous function. As used in this disclosure, “remotedevice” is an external device to flight controller. Additionally oralternatively, autonomous machine-learning model may include one or moreautonomous machine-learning processes that a field-programmable gatearray (FPGA) may or may not use in the generation of autonomousfunction. Autonomous machine-learning process may include, withoutlimitation machine learning processes such as simple linear regression,multiple linear regression, polynomial regression, support vectorregression, ridge regression, lasso regression, elastic net regression,decision tree regression, random forest regression, logistic regression,logistic classification, K-nearest neighbors, support vector machines,kernel support vector machines, naïve bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof.

In an embodiment, and still referring to FIG. 3 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 3 , flight controller may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller. Remote device and/or FPGAmay transmit a signal, bit, datum, or parameter to flight controllerthat at least relates to autonomous function. Additionally oralternatively, the remote device and/or FPGA may provide an updatedmachine-learning model. For example, and without limitation, an updatedmachine-learning model may be included of a firmware update, a softwareupdate, an autonomous machine-learning process correction, and the likethereof. As a non-limiting example a software update may incorporate anew simulation data that relates to a modified flight element.Additionally or alternatively, the updated machine learning model may betransmitted to the remote device and/or FPGA, wherein the remote deviceand/or FPGA may replace the autonomous machine-learning model with theupdated machine-learning model and generate the autonomous function as afunction of the flight element, pilot signal, and/or simulation datausing the updated machine-learning model. The updated machine-learningmodel may be transmitted by the remote device and/or FPGA and receivedby flight controller as a software update, firmware update, or correctedautonomous machine-learning model. For example, and without limitationautonomous machine learning model may utilize a neural netmachine-learning process, wherein the updated machine-learning model mayincorporate a gradient boosting machine-learning process.

Still referring to FIG. 3 , flight controller may include, be includedin, and/or communicate with a mobile device such as a mobile telephoneor smartphone. Further, flight controller may communicate with one ormore additional devices as described below in further detail via anetwork interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 3 , flight controller mayinclude, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controllermay include one or more flight controllers dedicated to data storage,security, distribution of traffic for load balancing, and the like.Flight controller may be configured to distribute one or more computingtasks as described below across a plurality of flight controllers, whichmay operate in parallel, in series, redundantly, or in any other mannerused for distribution of tasks or memory between computing devices. Forexample, and without limitation, flight controller may implement acontrol algorithm to distribute and/or command the plurality of flightcontrollers. As used in this disclosure a “control algorithm” is afinite sequence of well-defined computer implementable instructions thatmay determine the flight component of the plurality of flight componentsto be adjusted. For example, and without limitation, control algorithmmay include one or more algorithms that reduce and/or prevent aviationasymmetry. As a further non-limiting example, control algorithms mayinclude one or more models generated as a function of a softwareincluding, but not limited to Simulink by MathWorks, Natick, Mass., USA.In an embodiment, and without limitation, control algorithm may beconfigured to generate an auto-code, wherein an “auto-code,” is usedherein, is a code and/or algorithm that is generated as a function ofthe one or more models and/or software's. In another embodiment, controlalgorithm may be configured to produce a segmented control algorithm. Asused in this disclosure a “segmented control algorithm” is controlalgorithm that has been separated and/or parsed into discrete sections.For example, and without limitation, segmented control algorithm mayparse control algorithm into two or more segments, wherein each segmentof control algorithm may be performed by one or more flight controllersoperating on distinct flight components.

In an embodiment, and still referring to FIG. 3 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 108. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furtherincludes separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 3 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 312 and/or output language from logiccomponent 320, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 3 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 3 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 3 , flight controller may also be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of aircraft and/orcomputing device. Flight controller may include a distributer flightcontroller. As used in this disclosure a “distributer flight controller”is a component that adjusts and/or controls a plurality of flightcomponents as a function of a plurality of flight controllers. Forexample, distributer flight controller may include a flight controllerthat communicates with a plurality of additional flight controllersand/or clusters of flight controllers. In an embodiment, distributedflight control may include one or more neural networks. For example,neural network also known as an artificial neural network, is a networkof “nodes,” or data structures having one or more inputs, one or moreoutputs, and a function determining outputs based on inputs. Such nodesmay be organized in a network, such as without limitation aconvolutional neural network, including an input layer of nodes, one ormore intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training dataset are applied to the inputnodes, a suitable training algorithm (such as Levenberg-Marquardt,conjugate gradient, simulated annealing, or other algorithms) is thenused to adjust the connections and weights between nodes in adjacentlayers of the neural network to produce the desired values at the outputnodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 3 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 3 , flight controller may include asub-controller 340. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller may be and/or include adistributed flight controller made up of one or more sub-controllers.For example, and without limitation, sub-controller 340 may include anycontrollers and/or components thereof that are similar to distributedflight controller and/or flight controller as described above.Sub-controller 340 may include any component of any flight controller asdescribed above. Sub-controller 340 may be implemented in any mannersuitable for implementation of a flight controller as described above.As a further non-limiting example, sub-controller 340 may include one ormore processors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data across the distributedflight controller as described above. As a further non-limiting example,sub-controller 340 may include a controller that receives a signal froma first flight controller and/or first distributed flight controllercomponent and transmits the signal to a plurality of additionalsub-controllers and/or flight components.

Still referring to FIG. 3 , flight controller may include aco-controller 344. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller as componentsand/or nodes of a distributer flight controller as described above. Forexample, and without limitation, co-controller 344 may include one ormore controllers and/or components that are similar to flightcontroller. As a further non-limiting example, co-controller 344 mayinclude any controller and/or component that joins flight controller todistributer flight controller. As a further non-limiting example,co-controller 344 may include one or more processors, logic componentsand/or computing devices capable of receiving, processing, and/ortransmitting data to and/or from flight controller to distributed flightcontrol system. Co-controller 344 may include any component of anyflight controller as described above. Co-controller 344 may beimplemented in any manner suitable for implementation of a flightcontroller as described above.

In an embodiment, and with continued reference to FIG. 3 , flightcontroller may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller may be configured to perform a single stepor sequence repeatedly until a desired or commanded outcome is achieved;repetition of a step or a sequence of steps may be performed iterativelyand/or recursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Flight controller may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

Referring now to FIG. 4 , an exemplary embodiment of a machine-learningmodule 400 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A machine learning process is a process thatautomatedly uses training data 404 to generate an algorithm that will beperformed by a computing device/module to produce outputs 408 given dataprovided as inputs 412; this is in contrast to a non-machine learningsoftware program where the commands to be executed are determined inadvance by a user and written in a programming language.

Still referring to FIG. 4 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 404 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 404 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 404 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 404 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 404 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 404 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data404 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 4 ,training data 404 may include one or more elements that are notcategorized; that is, training data 404 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 404 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 404 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 404 used by machine-learning module 400 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 4 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 416. Training data classifier 416 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 400 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 404. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 4 , machine-learning module 400 may beconfigured to perform a lazy-learning process 420 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 404. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 404 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 4 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 424. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 424 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 424 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 404set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 4 , machine-learning algorithms may include atleast a supervised machine-learning process 428. At least a supervisedmachine-learning process 428, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 404. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process428 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 4 , machine learning processes may include atleast an unsupervised machine-learning processes 432. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 4 , machine-learning module 400 may be designedand configured to create a machine-learning model 424 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 4 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Now referring to FIG. 5 , an exemplary embodiment of a method 500 for anautomated sense and avoid system in an electric aircraft is presented.Electric aircraft may include, but without limitation, any of theaircraft as disclosed herein and described above with reference to atleast FIG. 1 .

Still referring to FIG. 5 , at step 505, method 500 includescommunicatively connecting a flight controller 208 to an electricaircraft. Flight controller may include, but without limitation, any ofthe controllers as disclosed herein and described above with referenceto at least FIGS. 2-3 .

Still referring to FIG. 5 , at step 510, method 500 includes receiving,at flight controller 208, a plurality of flight inputs 204 from asensor. Plurality of flight inputs includes identifying a specifiedgeographic location. Plurality of flight inputs includes some sort ofdigital signal. Plurality of flight inputs includes one or moretopographical inputs. Flight controller may include, but withoutlimitation, any of the controllers as disclosed herein and describedabove with reference to at least FIGS. 2-3 . Plurality of flight inputsmay include, but without limitation, any of the inputs as disclosedherein and described above with reference to at least FIG. 2 .

Still referring to FIG. 5 , at step 515, method 500 includesdetermining, at flight controller 208, an impact element 212 as afunction of plurality of flight inputs 204. Determining impact elementincludes identifying a length of time until the electric aircraftimpacts and/or collides with an object. Determining the impact elementincludes identifying a predictive degradation as a function of animpact. Predictive degradation includes one or more failure events thatare determined as a function of a collision. Flight controller mayinclude, but without limitation, any of the controllers as disclosedherein and described above with reference to at least FIGS. 2-3 . Impactelement may include, but without limitation, any of the impact elementsas disclosed herein and described above with reference to at least FIG.2 . Plurality of flight inputs may include, but without limitation, anyof the inputs as disclosed herein and described above with reference toat least FIG. 2 .

Still referring to FIG. 5 , at step 520, method 500 includes generating,at the flight controller, a navigation status 216 as a function ofimpact element 212. Flight controller may include, but withoutlimitation, any of the controllers as disclosed herein and describedabove with reference to at least FIGS. 2-3 . Navigation status mayinclude, but without limitation, any of the controllers as statusesherein and described above with reference to at least FIG. 2 . Impactelement may include, but without limitation, any of the impact elementsas disclosed herein and described above with reference to at least FIG.2 .

Still referring to FIG. 5 , at step 525, method 500 includes producing,at flight controller 208, a flight modification 220 as a function ofnavigation status 216. Flight modification includes a maneuver to beperformed by the electric aircraft to maintain a safe distance from theimpact element. Producing the flight modification includes a machinelearning process to determine one or more flight component adjustments.Flight controller may include, but without limitation, any of thecontrollers as disclosed herein and described above with reference to atleast FIGS. 2-3 . Flight modification may include, but withoutlimitation, any of the modifications as disclosed herein and describedabove with reference to at least FIG. 2 . Impact element may include,but without limitation, any of the impact elements as disclosed hereinand described above with reference to at least FIG. 2 .

Still referring to FIG. 5 , at step 530, method 500 includes initiating,at flight controller 208, flight modification 216 as a function of anautomated process. Automated process is configured to automatedlyperform the flight modification without pilot intervention Flightcontroller may include, but without limitation, any of the controllersas disclosed herein and described above with reference to at least FIGS.2-3 . Flight modification may include, but without limitation, any ofthe modifications as disclosed herein and described above with referenceto at least FIG. 2 .

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random-access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 6 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 600 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 600 includes a processor 604 and a memory608 that communicate with each other, and with other components, via abus 612. Bus 612 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 604 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 604 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 604 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating-pointunit (FPU), and/or system on a chip (SoC).

Memory 608 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 616 (BIOS), including basic routines that help totransfer information between elements within computer system 600, suchas during start-up, may be stored in memory 608. Memory 608 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 620 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 608 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 600 may also include a storage device 624. Examples of astorage device (e.g., storage device 624) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 624 may be connected to bus 612 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 624 (or one or morecomponents thereof) may be removably interfaced with computer system 600(e.g., via an external port connector (not shown)). Particularly,storage device 624 and an associated machine-readable medium 628 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 600. In one example, software 620 may reside, completelyor partially, within machine-readable medium 628. In another example,software 620 may reside, completely or partially, within processor 604.

Computer system 600 may also include an input device 632. In oneexample, a user of computer system 600 may enter commands and/or otherinformation into computer system 600 via input device 632. Examples ofan input device 632 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 632may be interfaced to bus 612 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 612, and any combinations thereof. Input device 632 mayinclude a touch screen interface that may be a part of or separate fromdisplay 636, discussed further below. Input device 632 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 600 via storage device 624 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 640. A network interfacedevice, such as network interface device 640, may be utilized forconnecting computer system 600 to one or more of a variety of networks,such as network 644, and one or more remote devices 648 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 644,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 620,etc.) may be communicated to and/or from computer system 600 via networkinterface device 640.

Computer system 600 may further include a video display adapter 652 forcommunicating a displayable image to a display device, such as displaydevice 636. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 652 and display device 636 may be utilized incombination with processor 604 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 600 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 612 via a peripheral interface 656. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve systems andmethods according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An automated sense and avoid system for a mannedelectric aircraft, the system comprising: a flight controllercommunicatively connected to an electric aircraft, wherein the flightcontroller is configured to: receive a plurality of flight inputs from asensor; identify an impact element as a function of the plurality offlight inputs, wherein identifying the impact element comprisesidentifying a predictive degradation as a function of the impact andwherein the predictive degradation includes one or more failure eventscomprising of any event that degrades an ability of one or more flightcomponents that are determined as a function of a collision, wherein thepredictive degradation comprises a degradation of a rotor of the one ormore flight components; generate a navigation status as a function of adirectional vector and the impact element; produce a flight modificationas a function of the navigation status; and initiate the flightmodification as a function of an automated process.
 2. The system ofclaim 1, wherein the plurality of flight inputs includes identifying aspecified geographic location.
 3. The system of claim 1, wherein theplurality of flight inputs includes a digital signal.
 4. The system ofclaim 1, wherein the plurality of flight inputs includes one or moretopographical inputs.
 5. The system of claim 1, wherein determining theimpact element includes identifying a length of time until the electricaircraft impacts an object.
 6. The system of claim 1, wherein the flightmodification includes a maneuver to be performed by the electricaircraft to maintain a safe distance from the impact element.
 7. Thesystem of claim 1, wherein producing the flight modification includes amachine learning process to determine one or more flight componentadjustments.
 8. The system of claim 1, wherein the automated process isconfigured to automatedly perform the flight modification without pilotintervention.
 9. The system of claim 1, wherein generating a navigationstatus as a function of the impact element includes displaying theimpact element to a pilot on board the aircraft for approval.
 10. Amethod for an automated sense and avoid system for a manned electricaircraft, the method comprising: communicatively connecting a flightcontroller to an electric aircraft; receiving, at the flight controller,a plurality of flight inputs from a sensor; identifying, at the flightcontroller, an impact element as a function of the plurality of flightinputs, wherein identifying the impact element comprises identifying apredictive degradation as a function of an impact and wherein thepredictive degradation includes one or more failure events comprising ofany event that degrades an ability of one or more flight components thatare determined as a function of a collision, wherein the predictivedegradation comprises a degradation of a rotor of the one of more flightcomponents; generating, at the flight controller, a navigation status asa function of a directional vector and the impact element; producing, atthe flight controller, a flight modification as a function of thenavigation status; and initiating, at the flight controller, the flightmodification as a function of an automated process.
 11. The method ofclaim 10, wherein the plurality of flight inputs includes identifying aspecified geographic location.
 12. The method of claim 10, wherein theplurality of flight inputs includes a digital signal.
 13. The method ofclaim 10, wherein the plurality of flight inputs includes one or moretopographical inputs.
 14. The method of claim 10, wherein determiningthe impact element includes identifying a length of time until theelectric aircraft impacts an object.
 15. The method of claim 10, whereinthe flight modification includes a maneuver to be performed by theelectric aircraft to maintain a safe distance from the impact element.16. The method of claim 10, wherein producing the flight modificationincludes a machine learning process to determine one or more flightcomponent adjustments.
 17. The method of claim 10, wherein the automatedprocess is configured to automatedly perform the flight modificationwithout pilot intervention.
 18. The method of claim 10, whereingenerating a navigation status as a function of the impact elementincludes displaying the impact element to a pilot on board the aircraftfor approval.